Self-repairing Learning Rule for Spiking Astrocyte-Neuron Networks

Junxiu Liu, Liam McDaid, Jim Harkin, John Wade, Shvan Karim, Anju P. Johnson, Alan G. Millard, David M. Halliday, Andy M. Tyrrell, Jon Timmis

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)
6 Downloads (Pure)

Abstract

In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.
Original languageEnglish
Title of host publicationNeural Information Processing
PublisherSpringer
Pages384-392
ISBN (Print)978-3-319-70095-3
DOIs
Publication statusE-pub ahead of print - 26 Oct 2017

Keywords

  • Astrocyte-neuron
  • network Learning window
  • Self-repair
  • Fault tolerance
  • adaptive
  • neural network

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    Liu, J., McDaid, L., Harkin, J., Wade, J., Karim, S., Johnson, A. P., Millard, A. G., Halliday, D. M., Tyrrell, A. M., & Timmis, J. (2017). Self-repairing Learning Rule for Spiking Astrocyte-Neuron Networks. In Neural Information Processing (pp. 384-392). Springer. https://doi.org/10.1007/978-3-319-70136-3_41